In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Two real world data sets, containing electricity load demands and foreign exchange market prices, are used to test several different methods, ranging from linear models with fixed parameters, to non-linear models which adapt both parameters and model order on-line. Training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. The results of our experiments show that there are advantages to be gained in tracking real world non-stationary data through the use of more complex adaptive models.
Ian T. Nabney ; Alan McLachlan and David Lowe, "Practical methods of tracking of nonstationary time series applied to real-world data", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, 152 (March 22, 1996); doi:10.1117/12.235906. Copyright 1996 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.235906
- RBF network, RAN, noise, plasticity, Kalman filter, training algorithm, tracking, real world non-stationary, complex adaptive models